Overview

Brought to you by YData

Dataset statistics

Number of variables81
Number of observations8
Missing cells184
Missing cells (%)28.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 KiB
Average record size in memory1.9 KiB

Variable types

Categorical27
Unsupported23
DateTime1
Text1
Numeric29

Alerts

UUID has constant value "1a0d36c1-153e-4983-ab58-3d879ec46458"Constant
Version has constant value "00.03.001"Constant
Name (en) has constant value "1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1"Constant
Compliance has constant value "'EN 15804+A2' / 'ISO 14025' / 'ISO 21930'"Constant
Background database(s) has constant value "'ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'"Constant
Location code has constant value "IT"Constant
Type has constant value "specific dataset"Constant
Reference year has constant value "2024"Constant
Valid until has constant value "2029"Constant
URL has constant value "https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001"Constant
Declaration owner has constant value "Betonrossi S.p.A."Constant
Publication date has constant value "2024-09-11 00:00:00"Constant
Registration number has constant value "EPDITALY0737"Constant
Registration authority has constant value "EPDItaly"Constant
Ref. quantity has constant value "1"Constant
Ref. unit has constant value "m3"Constant
Reference flow UUID has constant value "325f0ef1-ce83-4c24-9f6d-9d4ac4350487"Constant
Reference flow name has constant value "1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1"Constant
Carbon content (biogenic) in kg has constant value "0"Constant
Carbon content (biogenic) - packaging in kg has constant value "0"Constant
CRU has constant value "0"Constant
MER has constant value "0"Constant
EEE has constant value "0"Constant
EET has constant value "0"Constant
ADPE (A2) is highly overall correlated with ADPF (A2) and 27 other fieldsHigh correlation
ADPF (A2) is highly overall correlated with ADPE (A2) and 27 other fieldsHigh correlation
AP (A2) is highly overall correlated with ADPE (A2) and 26 other fieldsHigh correlation
EPfreshwater (A2) is highly overall correlated with ADPE (A2) and 27 other fieldsHigh correlation
EPmarine (A2) is highly overall correlated with ADPE (A2) and 27 other fieldsHigh correlation
EPterrestrial (A2) is highly overall correlated with ADPE (A2) and 26 other fieldsHigh correlation
ETPfw (A2) is highly overall correlated with ADPE (A2) and 22 other fieldsHigh correlation
FW is highly overall correlated with ADPE (A2) and 29 other fieldsHigh correlation
GWPbiogenic (A2) is highly overall correlated with ETPfw (A2) and 4 other fieldsHigh correlation
GWPfossil (A2) is highly overall correlated with ADPE (A2) and 25 other fieldsHigh correlation
GWPluluc (A2) is highly overall correlated with ADPE (A2) and 24 other fieldsHigh correlation
GWPtotal (A2) is highly overall correlated with ADPE (A2) and 25 other fieldsHigh correlation
HTPc (A2) is highly overall correlated with ADPE (A2) and 19 other fieldsHigh correlation
HTPnc (A2) is highly overall correlated with ADPE (A2) and 8 other fieldsHigh correlation
HWD is highly overall correlated with ADPE (A2) and 25 other fieldsHigh correlation
IRP (A2) is highly overall correlated with ADPE (A2) and 27 other fieldsHigh correlation
MFR is highly overall correlated with ADPF (A2) and 21 other fieldsHigh correlation
NHWD is highly overall correlated with FW and 2 other fieldsHigh correlation
NRSF is highly overall correlated with ADPE (A2) and 16 other fieldsHigh correlation
ODP (A2) is highly overall correlated with ADPE (A2) and 24 other fieldsHigh correlation
PENRE is highly overall correlated with ADPE (A2) and 27 other fieldsHigh correlation
PENRM is highly overall correlated with ADPE (A2) and 24 other fieldsHigh correlation
PENRT is highly overall correlated with ADPE (A2) and 27 other fieldsHigh correlation
PERE is highly overall correlated with ADPE (A2) and 27 other fieldsHigh correlation
PERM is highly overall correlated with ADPE (A2) and 24 other fieldsHigh correlation
PERT is highly overall correlated with ADPE (A2) and 27 other fieldsHigh correlation
PM (A2) is highly overall correlated with ADPE (A2) and 25 other fieldsHigh correlation
POCP (A2) is highly overall correlated with ADPE (A2) and 26 other fieldsHigh correlation
RSF is highly overall correlated with ADPE (A2) and 16 other fieldsHigh correlation
RWD is highly overall correlated with ADPE (A2) and 25 other fieldsHigh correlation
SM is highly overall correlated with ADPE (A2) and 16 other fieldsHigh correlation
SOP (A2) is highly overall correlated with EPfreshwater (A2) and 13 other fieldsHigh correlation
WDP (A2) is highly overall correlated with ADPE (A2) and 29 other fieldsHigh correlation
Name (it) has 8 (100.0%) missing valuesMissing
Category (original) has 8 (100.0%) missing valuesMissing
Predecessor UUID has 8 (100.0%) missing valuesMissing
Predecessor Version has 8 (100.0%) missing valuesMissing
Predecessor URL has 8 (100.0%) missing valuesMissing
Bulk Density (kg/m3) has 8 (100.0%) missing valuesMissing
Grammage (kg/m2) has 8 (100.0%) missing valuesMissing
Gross Density (kg/m3) has 8 (100.0%) missing valuesMissing
Layer Thickness (m) has 8 (100.0%) missing valuesMissing
Productiveness (m2) has 8 (100.0%) missing valuesMissing
Linear Density (kg/m) has 8 (100.0%) missing valuesMissing
Weight Per Piece (kg) has 8 (100.0%) missing valuesMissing
Conversion factor to 1kg has 8 (100.0%) missing valuesMissing
Scenario has 8 (100.0%) missing valuesMissing
Scenario Description has 8 (100.0%) missing valuesMissing
GWP has 8 (100.0%) missing valuesMissing
ODP has 8 (100.0%) missing valuesMissing
POCP has 8 (100.0%) missing valuesMissing
AP has 8 (100.0%) missing valuesMissing
EP has 8 (100.0%) missing valuesMissing
ADPE has 8 (100.0%) missing valuesMissing
ADPF has 8 (100.0%) missing valuesMissing
Unnamed: 80 has 8 (100.0%) missing valuesMissing
Module has unique valuesUnique
PERE has unique valuesUnique
PERM has unique valuesUnique
PERT has unique valuesUnique
PENRE has unique valuesUnique
PENRM has unique valuesUnique
PENRT has unique valuesUnique
FW has unique valuesUnique
HWD has unique valuesUnique
NHWD has unique valuesUnique
RWD has unique valuesUnique
AP (A2) has unique valuesUnique
GWPtotal (A2) has unique valuesUnique
GWPbiogenic (A2) has unique valuesUnique
GWPfossil (A2) has unique valuesUnique
GWPluluc (A2) has unique valuesUnique
ETPfw (A2) has unique valuesUnique
PM (A2) has unique valuesUnique
EPmarine (A2) has unique valuesUnique
EPfreshwater (A2) has unique valuesUnique
EPterrestrial (A2) has unique valuesUnique
HTPc (A2) has unique valuesUnique
HTPnc (A2) has unique valuesUnique
IRP (A2) has unique valuesUnique
SOP (A2) has unique valuesUnique
ODP (A2) has unique valuesUnique
POCP (A2) has unique valuesUnique
ADPF (A2) has unique valuesUnique
ADPE (A2) has unique valuesUnique
WDP (A2) has unique valuesUnique
Name (it) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Category (original) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Predecessor UUID is an unsupported type, check if it needs cleaning or further analysisUnsupported
Predecessor Version is an unsupported type, check if it needs cleaning or further analysisUnsupported
Predecessor URL is an unsupported type, check if it needs cleaning or further analysisUnsupported
Bulk Density (kg/m3) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Grammage (kg/m2) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Gross Density (kg/m3) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Layer Thickness (m) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Productiveness (m2) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Linear Density (kg/m) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Weight Per Piece (kg) is an unsupported type, check if it needs cleaning or further analysisUnsupported
Conversion factor to 1kg is an unsupported type, check if it needs cleaning or further analysisUnsupported
Scenario is an unsupported type, check if it needs cleaning or further analysisUnsupported
Scenario Description is an unsupported type, check if it needs cleaning or further analysisUnsupported
GWP is an unsupported type, check if it needs cleaning or further analysisUnsupported
ODP is an unsupported type, check if it needs cleaning or further analysisUnsupported
POCP is an unsupported type, check if it needs cleaning or further analysisUnsupported
AP is an unsupported type, check if it needs cleaning or further analysisUnsupported
EP is an unsupported type, check if it needs cleaning or further analysisUnsupported
ADPE is an unsupported type, check if it needs cleaning or further analysisUnsupported
ADPF is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 80 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-11-25 13:49:51.312188
Analysis finished2025-11-25 13:51:26.391685
Duration1 minute and 35.08 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

UUID
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size872.0 B
1a0d36c1-153e-4983-ab58-3d879ec46458

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters288
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1a0d36c1-153e-4983-ab58-3d879ec46458
2nd row1a0d36c1-153e-4983-ab58-3d879ec46458
3rd row1a0d36c1-153e-4983-ab58-3d879ec46458
4th row1a0d36c1-153e-4983-ab58-3d879ec46458
5th row1a0d36c1-153e-4983-ab58-3d879ec46458

Common Values

ValueCountFrequency (%)
1a0d36c1-153e-4983-ab58-3d879ec464588
100.0%

Length

2025-11-25T14:51:26.585920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:26.722231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1a0d36c1-153e-4983-ab58-3d879ec464588
100.0%

Most occurring characters

ValueCountFrequency (%)
332
11.1%
832
11.1%
-32
11.1%
524
8.3%
424
8.3%
124
8.3%
a16
 
5.6%
e16
 
5.6%
c16
 
5.6%
d16
 
5.6%
Other values (5)56
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
332
11.1%
832
11.1%
-32
11.1%
524
8.3%
424
8.3%
124
8.3%
a16
 
5.6%
e16
 
5.6%
c16
 
5.6%
d16
 
5.6%
Other values (5)56
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
332
11.1%
832
11.1%
-32
11.1%
524
8.3%
424
8.3%
124
8.3%
a16
 
5.6%
e16
 
5.6%
c16
 
5.6%
d16
 
5.6%
Other values (5)56
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
332
11.1%
832
11.1%
-32
11.1%
524
8.3%
424
8.3%
124
8.3%
a16
 
5.6%
e16
 
5.6%
c16
 
5.6%
d16
 
5.6%
Other values (5)56
19.4%

Version
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size656.0 B
00.03.001

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters72
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00.03.001
2nd row00.03.001
3rd row00.03.001
4th row00.03.001
5th row00.03.001

Common Values

ValueCountFrequency (%)
00.03.0018
100.0%

Length

2025-11-25T14:51:26.845875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:26.960383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
00.03.0018
100.0%

Most occurring characters

ValueCountFrequency (%)
040
55.6%
.16
 
22.2%
38
 
11.1%
18
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)72
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
040
55.6%
.16
 
22.2%
38
 
11.1%
18
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)72
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
040
55.6%
.16
 
22.2%
38
 
11.1%
18
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)72
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
040
55.6%
.16
 
22.2%
38
 
11.1%
18
 
11.1%

Name (en)
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1

Length

Max length60
Median length60
Mean length60
Min length60

Characters and Unicode

Total characters480
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1
2nd row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1
3rd row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1
4th row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1
5th row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1

Common Values

ValueCountFrequency (%)
1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC18
100.0%

Length

2025-11-25T14:51:27.094742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:27.218126image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
18
16.7%
ready-mixed8
16.7%
concrete8
16.7%
mixtures8
16.7%
multibeton8
16.7%
r30c3d16s4xc2xc18
16.7%

Most occurring characters

ValueCountFrequency (%)
e48
 
10.0%
40
 
8.3%
t32
 
6.7%
i24
 
5.0%
124
 
5.0%
C24
 
5.0%
R16
 
3.3%
n16
 
3.3%
o16
 
3.3%
x16
 
3.3%
Other values (21)224
46.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e48
 
10.0%
40
 
8.3%
t32
 
6.7%
i24
 
5.0%
124
 
5.0%
C24
 
5.0%
R16
 
3.3%
n16
 
3.3%
o16
 
3.3%
x16
 
3.3%
Other values (21)224
46.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e48
 
10.0%
40
 
8.3%
t32
 
6.7%
i24
 
5.0%
124
 
5.0%
C24
 
5.0%
R16
 
3.3%
n16
 
3.3%
o16
 
3.3%
x16
 
3.3%
Other values (21)224
46.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e48
 
10.0%
40
 
8.3%
t32
 
6.7%
i24
 
5.0%
124
 
5.0%
C24
 
5.0%
R16
 
3.3%
n16
 
3.3%
o16
 
3.3%
x16
 
3.3%
Other values (21)224
46.7%

Name (it)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Category (original)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Compliance
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size912.0 B
'EN 15804+A2' / 'ISO 14025' / 'ISO 21930'

Length

Max length41
Median length41
Mean length41
Min length41

Characters and Unicode

Total characters328
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'EN 15804+A2' / 'ISO 14025' / 'ISO 21930'
2nd row'EN 15804+A2' / 'ISO 14025' / 'ISO 21930'
3rd row'EN 15804+A2' / 'ISO 14025' / 'ISO 21930'
4th row'EN 15804+A2' / 'ISO 14025' / 'ISO 21930'
5th row'EN 15804+A2' / 'ISO 14025' / 'ISO 21930'

Common Values

ValueCountFrequency (%)
'EN 15804+A2' / 'ISO 14025' / 'ISO 21930'8
100.0%

Length

2025-11-25T14:51:27.346152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:27.465948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
iso16
25.0%
16
25.0%
15804+a28
12.5%
en8
12.5%
140258
12.5%
219308
12.5%

Most occurring characters

ValueCountFrequency (%)
56
17.1%
'48
14.6%
024
 
7.3%
124
 
7.3%
224
 
7.3%
O16
 
4.9%
416
 
4.9%
I16
 
4.9%
/16
 
4.9%
516
 
4.9%
Other values (8)72
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
56
17.1%
'48
14.6%
024
 
7.3%
124
 
7.3%
224
 
7.3%
O16
 
4.9%
416
 
4.9%
I16
 
4.9%
/16
 
4.9%
516
 
4.9%
Other values (8)72
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
56
17.1%
'48
14.6%
024
 
7.3%
124
 
7.3%
224
 
7.3%
O16
 
4.9%
416
 
4.9%
I16
 
4.9%
/16
 
4.9%
516
 
4.9%
Other values (8)72
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
56
17.1%
'48
14.6%
024
 
7.3%
124
 
7.3%
224
 
7.3%
O16
 
4.9%
416
 
4.9%
I16
 
4.9%
/16
 
4.9%
516
 
4.9%
Other values (8)72
22.0%

Background database(s)
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
'ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'

Length

Max length59
Median length59
Mean length59
Min length59

Characters and Unicode

Total characters472
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row'ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'
2nd row'ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'
3rd row'ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'
4th row'ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'
5th row'ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'

Common Values

ValueCountFrequency (%)
'ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'8
100.0%

Length

2025-11-25T14:51:27.586131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:27.706074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ecoinvent8
33.3%
database8
33.3%
b497a91f-e14b-4b69-8f28-f50eb15767668
33.3%

Most occurring characters

ValueCountFrequency (%)
e40
 
8.5%
b40
 
8.5%
-32
 
6.8%
632
 
6.8%
a32
 
6.8%
724
 
5.1%
424
 
5.1%
924
 
5.1%
124
 
5.1%
f24
 
5.1%
Other values (16)176
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)472
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e40
 
8.5%
b40
 
8.5%
-32
 
6.8%
632
 
6.8%
a32
 
6.8%
724
 
5.1%
424
 
5.1%
924
 
5.1%
124
 
5.1%
f24
 
5.1%
Other values (16)176
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)472
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e40
 
8.5%
b40
 
8.5%
-32
 
6.8%
632
 
6.8%
a32
 
6.8%
724
 
5.1%
424
 
5.1%
924
 
5.1%
124
 
5.1%
f24
 
5.1%
Other values (16)176
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)472
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e40
 
8.5%
b40
 
8.5%
-32
 
6.8%
632
 
6.8%
a32
 
6.8%
724
 
5.1%
424
 
5.1%
924
 
5.1%
124
 
5.1%
f24
 
5.1%
Other values (16)176
37.3%

Location code
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size600.0 B
IT

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters16
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIT
2nd rowIT
3rd rowIT
4th rowIT
5th rowIT

Common Values

ValueCountFrequency (%)
IT8
100.0%

Length

2025-11-25T14:51:27.815827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:27.915782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
it8
100.0%

Most occurring characters

ValueCountFrequency (%)
I8
50.0%
T8
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I8
50.0%
T8
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I8
50.0%
T8
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I8
50.0%
T8
50.0%

Type
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size712.0 B
specific dataset

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters128
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowspecific dataset
2nd rowspecific dataset
3rd rowspecific dataset
4th rowspecific dataset
5th rowspecific dataset

Common Values

ValueCountFrequency (%)
specific dataset8
100.0%

Length

2025-11-25T14:51:28.026017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:28.130763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
specific8
50.0%
dataset8
50.0%

Most occurring characters

ValueCountFrequency (%)
s16
12.5%
e16
12.5%
a16
12.5%
c16
12.5%
i16
12.5%
t16
12.5%
p8
6.2%
f8
6.2%
d8
6.2%
8
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)128
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s16
12.5%
e16
12.5%
a16
12.5%
c16
12.5%
i16
12.5%
t16
12.5%
p8
6.2%
f8
6.2%
d8
6.2%
8
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)128
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s16
12.5%
e16
12.5%
a16
12.5%
c16
12.5%
i16
12.5%
t16
12.5%
p8
6.2%
f8
6.2%
d8
6.2%
8
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)128
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s16
12.5%
e16
12.5%
a16
12.5%
c16
12.5%
i16
12.5%
t16
12.5%
p8
6.2%
f8
6.2%
d8
6.2%
8
6.2%

Reference year
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size616.0 B
2024

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters32
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
20248
100.0%

Length

2025-11-25T14:51:28.257377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:28.369925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
20248
100.0%

Most occurring characters

ValueCountFrequency (%)
216
50.0%
08
25.0%
48
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)32
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
216
50.0%
08
25.0%
48
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
216
50.0%
08
25.0%
48
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
216
50.0%
08
25.0%
48
25.0%

Valid until
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size616.0 B
2029

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters32
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2029
2nd row2029
3rd row2029
4th row2029
5th row2029

Common Values

ValueCountFrequency (%)
20298
100.0%

Length

2025-11-25T14:51:28.490351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:28.602249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
20298
100.0%

Most occurring characters

ValueCountFrequency (%)
216
50.0%
08
25.0%
98
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)32
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
216
50.0%
08
25.0%
98
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
216
50.0%
08
25.0%
98
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
216
50.0%
08
25.0%
98
25.0%

URL
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001

Length

Max length98
Median length98
Mean length98
Min length98

Characters and Unicode

Total characters784
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001
2nd rowhttps://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001
3rd rowhttps://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001
4th rowhttps://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001
5th rowhttps://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001

Common Values

ValueCountFrequency (%)
https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.0018
100.0%

Length

2025-11-25T14:51:28.715717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:28.821176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.0018
100.0%

Most occurring characters

ValueCountFrequency (%)
e72
 
9.2%
s48
 
6.1%
048
 
6.1%
/40
 
5.1%
340
 
5.1%
.32
 
4.1%
r32
 
4.1%
o32
 
4.1%
c32
 
4.1%
132
 
4.1%
Other values (22)376
48.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e72
 
9.2%
s48
 
6.1%
048
 
6.1%
/40
 
5.1%
340
 
5.1%
.32
 
4.1%
r32
 
4.1%
o32
 
4.1%
c32
 
4.1%
132
 
4.1%
Other values (22)376
48.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e72
 
9.2%
s48
 
6.1%
048
 
6.1%
/40
 
5.1%
340
 
5.1%
.32
 
4.1%
r32
 
4.1%
o32
 
4.1%
c32
 
4.1%
132
 
4.1%
Other values (22)376
48.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e72
 
9.2%
s48
 
6.1%
048
 
6.1%
/40
 
5.1%
340
 
5.1%
.32
 
4.1%
r32
 
4.1%
o32
 
4.1%
c32
 
4.1%
132
 
4.1%
Other values (22)376
48.0%

Declaration owner
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size720.0 B
Betonrossi S.p.A.

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters136
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBetonrossi S.p.A.
2nd rowBetonrossi S.p.A.
3rd rowBetonrossi S.p.A.
4th rowBetonrossi S.p.A.
5th rowBetonrossi S.p.A.

Common Values

ValueCountFrequency (%)
Betonrossi S.p.A.8
100.0%

Length

2025-11-25T14:51:28.930759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:29.034500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
betonrossi8
50.0%
s.p.a8
50.0%

Most occurring characters

ValueCountFrequency (%)
.24
17.6%
s16
11.8%
o16
11.8%
B8
 
5.9%
e8
 
5.9%
n8
 
5.9%
t8
 
5.9%
i8
 
5.9%
r8
 
5.9%
8
 
5.9%
Other values (3)24
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.24
17.6%
s16
11.8%
o16
11.8%
B8
 
5.9%
e8
 
5.9%
n8
 
5.9%
t8
 
5.9%
i8
 
5.9%
r8
 
5.9%
8
 
5.9%
Other values (3)24
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.24
17.6%
s16
11.8%
o16
11.8%
B8
 
5.9%
e8
 
5.9%
n8
 
5.9%
t8
 
5.9%
i8
 
5.9%
r8
 
5.9%
8
 
5.9%
Other values (3)24
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.24
17.6%
s16
11.8%
o16
11.8%
B8
 
5.9%
e8
 
5.9%
n8
 
5.9%
t8
 
5.9%
i8
 
5.9%
r8
 
5.9%
8
 
5.9%
Other values (3)24
17.6%

Publication date
Date

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size192.0 B
Minimum2024-09-11 00:00:00
Maximum2024-09-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T14:51:29.126023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:29.221116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Registration number
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size680.0 B
EPDITALY0737

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters96
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEPDITALY0737
2nd rowEPDITALY0737
3rd rowEPDITALY0737
4th rowEPDITALY0737
5th rowEPDITALY0737

Common Values

ValueCountFrequency (%)
EPDITALY07378
100.0%

Length

2025-11-25T14:51:29.336211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:29.425897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
epditaly07378
100.0%

Most occurring characters

ValueCountFrequency (%)
716
16.7%
P8
8.3%
E8
8.3%
D8
8.3%
I8
8.3%
A8
8.3%
T8
8.3%
L8
8.3%
Y8
8.3%
08
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
716
16.7%
P8
8.3%
E8
8.3%
D8
8.3%
I8
8.3%
A8
8.3%
T8
8.3%
L8
8.3%
Y8
8.3%
08
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
716
16.7%
P8
8.3%
E8
8.3%
D8
8.3%
I8
8.3%
A8
8.3%
T8
8.3%
L8
8.3%
Y8
8.3%
08
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)96
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
716
16.7%
P8
8.3%
E8
8.3%
D8
8.3%
I8
8.3%
A8
8.3%
T8
8.3%
L8
8.3%
Y8
8.3%
08
8.3%

Registration authority
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size648.0 B
EPDItaly

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters64
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEPDItaly
2nd rowEPDItaly
3rd rowEPDItaly
4th rowEPDItaly
5th rowEPDItaly

Common Values

ValueCountFrequency (%)
EPDItaly8
100.0%

Length

2025-11-25T14:51:29.536122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:29.635729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
epditaly8
100.0%

Most occurring characters

ValueCountFrequency (%)
E8
12.5%
P8
12.5%
D8
12.5%
I8
12.5%
t8
12.5%
a8
12.5%
l8
12.5%
y8
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)64
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E8
12.5%
P8
12.5%
D8
12.5%
I8
12.5%
t8
12.5%
a8
12.5%
l8
12.5%
y8
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)64
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E8
12.5%
P8
12.5%
D8
12.5%
I8
12.5%
t8
12.5%
a8
12.5%
l8
12.5%
y8
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)64
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E8
12.5%
P8
12.5%
D8
12.5%
I8
12.5%
t8
12.5%
a8
12.5%
l8
12.5%
y8
12.5%

Predecessor UUID
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Predecessor Version
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Predecessor URL
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Ref. quantity
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size592.0 B
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
18
100.0%

Length

2025-11-25T14:51:29.736909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:29.828681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
18
100.0%

Most occurring characters

ValueCountFrequency (%)
18
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18
100.0%

Ref. unit
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size600.0 B
m3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters16
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowm3
2nd rowm3
3rd rowm3
4th rowm3
5th rowm3

Common Values

ValueCountFrequency (%)
m38
100.0%

Length

2025-11-25T14:51:29.934899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:30.036102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
m38
100.0%

Most occurring characters

ValueCountFrequency (%)
m8
50.0%
38
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m8
50.0%
38
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m8
50.0%
38
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m8
50.0%
38
50.0%

Reference flow UUID
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size872.0 B
325f0ef1-ce83-4c24-9f6d-9d4ac4350487

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters288
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row325f0ef1-ce83-4c24-9f6d-9d4ac4350487
2nd row325f0ef1-ce83-4c24-9f6d-9d4ac4350487
3rd row325f0ef1-ce83-4c24-9f6d-9d4ac4350487
4th row325f0ef1-ce83-4c24-9f6d-9d4ac4350487
5th row325f0ef1-ce83-4c24-9f6d-9d4ac4350487

Common Values

ValueCountFrequency (%)
325f0ef1-ce83-4c24-9f6d-9d4ac43504878
100.0%

Length

2025-11-25T14:51:30.140066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:30.240830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
325f0ef1-ce83-4c24-9f6d-9d4ac43504878
100.0%

Most occurring characters

ValueCountFrequency (%)
440
13.9%
-32
11.1%
f24
 
8.3%
324
 
8.3%
c24
 
8.3%
016
 
5.6%
516
 
5.6%
216
 
5.6%
d16
 
5.6%
e16
 
5.6%
Other values (6)64
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
440
13.9%
-32
11.1%
f24
 
8.3%
324
 
8.3%
c24
 
8.3%
016
 
5.6%
516
 
5.6%
216
 
5.6%
d16
 
5.6%
e16
 
5.6%
Other values (6)64
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
440
13.9%
-32
11.1%
f24
 
8.3%
324
 
8.3%
c24
 
8.3%
016
 
5.6%
516
 
5.6%
216
 
5.6%
d16
 
5.6%
e16
 
5.6%
Other values (6)64
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
440
13.9%
-32
11.1%
f24
 
8.3%
324
 
8.3%
c24
 
8.3%
016
 
5.6%
516
 
5.6%
216
 
5.6%
d16
 
5.6%
e16
 
5.6%
Other values (6)64
22.2%

Reference flow name
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1

Length

Max length60
Median length60
Mean length60
Min length60

Characters and Unicode

Total characters480
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1
2nd row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1
3rd row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1
4th row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1
5th row1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1

Common Values

ValueCountFrequency (%)
1 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC18
100.0%

Length

2025-11-25T14:51:30.345216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:30.439369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
18
16.7%
ready-mixed8
16.7%
concrete8
16.7%
mixtures8
16.7%
multibeton8
16.7%
r30c3d16s4xc2xc18
16.7%

Most occurring characters

ValueCountFrequency (%)
e48
 
10.0%
40
 
8.3%
t32
 
6.7%
i24
 
5.0%
124
 
5.0%
C24
 
5.0%
R16
 
3.3%
n16
 
3.3%
o16
 
3.3%
x16
 
3.3%
Other values (21)224
46.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e48
 
10.0%
40
 
8.3%
t32
 
6.7%
i24
 
5.0%
124
 
5.0%
C24
 
5.0%
R16
 
3.3%
n16
 
3.3%
o16
 
3.3%
x16
 
3.3%
Other values (21)224
46.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e48
 
10.0%
40
 
8.3%
t32
 
6.7%
i24
 
5.0%
124
 
5.0%
C24
 
5.0%
R16
 
3.3%
n16
 
3.3%
o16
 
3.3%
x16
 
3.3%
Other values (21)224
46.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e48
 
10.0%
40
 
8.3%
t32
 
6.7%
i24
 
5.0%
124
 
5.0%
C24
 
5.0%
R16
 
3.3%
n16
 
3.3%
o16
 
3.3%
x16
 
3.3%
Other values (21)224
46.7%

Bulk Density (kg/m3)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Grammage (kg/m2)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Gross Density (kg/m3)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Layer Thickness (m)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Productiveness (m2)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Linear Density (kg/m)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Weight Per Piece (kg)
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Conversion factor to 1kg
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Carbon content (biogenic) in kg
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size592.0 B
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08
100.0%

Length

2025-11-25T14:51:30.548535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:30.648037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
08
100.0%

Most occurring characters

ValueCountFrequency (%)
08
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08
100.0%
Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size592.0 B
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08
100.0%

Length

2025-11-25T14:51:30.829247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:30.925689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
08
100.0%

Most occurring characters

ValueCountFrequency (%)
08
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08
100.0%

Module
Text

Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size599.0 B
2025-11-25T14:51:31.016108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.875
Min length1

Characters and Unicode

Total characters15
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)100.0%

Sample

1st rowA1
2nd rowA2
3rd rowA3
4th rowC1
5th rowC2
ValueCountFrequency (%)
a11
12.5%
a21
12.5%
a31
12.5%
c11
12.5%
c21
12.5%
c31
12.5%
c41
12.5%
d1
12.5%
2025-11-25T14:51:31.264582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C4
26.7%
A3
20.0%
12
13.3%
22
13.3%
32
13.3%
41
 
6.7%
D1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C4
26.7%
A3
20.0%
12
13.3%
22
13.3%
32
13.3%
41
 
6.7%
D1
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C4
26.7%
A3
20.0%
12
13.3%
22
13.3%
32
13.3%
41
 
6.7%
D1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C4
26.7%
A3
20.0%
12
13.3%
22
13.3%
32
13.3%
41
 
6.7%
D1
 
6.7%

Scenario
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Scenario Description
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

GWP
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

ODP
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

POCP
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

AP
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

EP
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

ADPE
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

ADPF
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

PERE
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4365913
Minimum-1.1372624
Maximum23.726206
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:31.365937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.1372624
5-th percentile-0.73834823
Q10.0033684877
median0.17096257
Q31.3508773
95-th percentile16.868167
Maximum23.726206
Range24.863468
Interquartile range (IQR)1.3475088

Descriptive statistics

Standard deviation8.3414338
Coefficient of variation (CV)2.4272405
Kurtosis7.1744233
Mean3.4365913
Median Absolute Deviation (MAD)0.21070382
Skewness2.6514967
Sum27.492731
Variance69.579517
MonotonicityNot monotonic
2025-11-25T14:51:31.476145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
23.72620611
12.5%
0.14207966571
12.5%
0.0024923659351
12.5%
0.19984547531
12.5%
0.42390001181
12.5%
4.1318090361
12.5%
0.0036605282391
12.5%
-1.137262391
12.5%
ValueCountFrequency (%)
-1.137262391
12.5%
0.0024923659351
12.5%
0.0036605282391
12.5%
0.14207966571
12.5%
0.19984547531
12.5%
0.42390001181
12.5%
4.1318090361
12.5%
23.72620611
12.5%
ValueCountFrequency (%)
23.72620611
12.5%
4.1318090361
12.5%
0.42390001181
12.5%
0.19984547531
12.5%
0.14207966571
12.5%
0.0036605282391
12.5%
0.0024923659351
12.5%
-1.137262391
12.5%

PERM
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9890629
Minimum-0.92940659
Maximum31.587009
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:31.579058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.92940659
5-th percentile-0.60391967
Q10.0029577505
median0.026145915
Q30.33253895
95-th percentile20.927883
Maximum31.587009
Range32.516415
Interquartile range (IQR)0.3295812

Descriptive statistics

Standard deviation11.164932
Coefficient of variation (CV)2.7988859
Kurtosis7.9412275
Mean3.9890629
Median Absolute Deviation (MAD)0.032687142
Skewness2.814634
Sum31.912504
Variance124.65571
MonotonicityNot monotonic
2025-11-25T14:51:31.686096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
31.587008811
12.5%
0.022098037131
12.5%
0.0005560412191
12.5%
0.030193792791
12.5%
0.065930324991
12.5%
1.1323648251
12.5%
0.0037583202761
12.5%
-0.92940658611
12.5%
ValueCountFrequency (%)
-0.92940658611
12.5%
0.0005560412191
12.5%
0.0037583202761
12.5%
0.022098037131
12.5%
0.030193792791
12.5%
0.065930324991
12.5%
1.1323648251
12.5%
31.587008811
12.5%
ValueCountFrequency (%)
31.587008811
12.5%
1.1323648251
12.5%
0.065930324991
12.5%
0.030193792791
12.5%
0.022098037131
12.5%
0.0037583202761
12.5%
0.0005560412191
12.5%
-0.92940658611
12.5%

PERT
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4266042
Minimum-2.066669
Maximum55.320814
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:31.795135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.066669
5-th percentile-1.3422679
Q10.0063262381
median0.19710848
Q31.6834162
95-th percentile37.80099
Maximum55.320814
Range57.387483
Interquartile range (IQR)1.67709

Descriptive statistics

Standard deviation19.462269
Coefficient of variation (CV)2.6206148
Kurtosis7.7264894
Mean7.4266042
Median Absolute Deviation (MAD)0.24339097
Skewness2.7669922
Sum59.412833
Variance378.77991
MonotonicityNot monotonic
2025-11-25T14:51:31.891342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
55.320813891
12.5%
0.16417769921
12.5%
0.0030484071511
12.5%
0.23003926811
12.5%
0.48983034871
12.5%
5.2641737441
12.5%
0.0074188484661
12.5%
-2.0666689531
12.5%
ValueCountFrequency (%)
-2.0666689531
12.5%
0.0030484071511
12.5%
0.0074188484661
12.5%
0.16417769921
12.5%
0.23003926811
12.5%
0.48983034871
12.5%
5.2641737441
12.5%
55.320813891
12.5%
ValueCountFrequency (%)
55.320813891
12.5%
5.2641737441
12.5%
0.48983034871
12.5%
0.23003926811
12.5%
0.16417769921
12.5%
0.0074188484661
12.5%
0.0030484071511
12.5%
-2.0666689531
12.5%

PENRE
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.20569
Minimum-113.94236
Maximum1193.3458
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:32.000999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-113.94236
5-th percentile-73.944068
Q11.4122435
median78.217037
Q3143.71845
95-th percentile845.01404
Maximum1193.3458
Range1307.2882
Interquartile range (IQR)142.30621

Descriptive statistics

Standard deviation413.99687
Coefficient of variation (CV)2.1208237
Kurtosis6.8295604
Mean195.20569
Median Absolute Deviation (MAD)77.16272
Skewness2.5410919
Sum1561.6456
Variance171393.41
MonotonicityNot monotonic
2025-11-25T14:51:32.115881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1193.3458281
12.5%
66.401759211
12.5%
0.33846436681
12.5%
125.5872191
12.5%
198.11215021
12.5%
90.032315481
12.5%
1.7701698441
12.5%
-113.9423551
12.5%
ValueCountFrequency (%)
-113.9423551
12.5%
0.33846436681
12.5%
1.7701698441
12.5%
66.401759211
12.5%
90.032315481
12.5%
125.5872191
12.5%
198.11215021
12.5%
1193.3458281
12.5%
ValueCountFrequency (%)
1193.3458281
12.5%
198.11215021
12.5%
125.5872191
12.5%
90.032315481
12.5%
66.401759211
12.5%
1.7701698441
12.5%
0.33846436681
12.5%
-113.9423551
12.5%

PENRM
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9616093
Minimum-0.00030197058
Maximum15.6927
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:32.219830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.00030197058
5-th percentile-0.00019611074
Q14.5500368 × 10-6
median1.3561872 × 10-5
Q30.00016618409
95-th percentile10.200371
Maximum15.6927
Range15.693002
Interquartile range (IQR)0.00016163405

Descriptive statistics

Standard deviation5.5481985
Coefficient of variation (CV)2.8283912
Kurtosis8
Mean1.9616093
Median Absolute Deviation (MAD)5.5194967 × 10-5
Skewness2.8284271
Sum15.692875
Variance30.782506
MonotonicityNot monotonic
2025-11-25T14:51:32.336066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
15.692699991
12.5%
6.808959 × 10-61
12.5%
4.86122 × 10-71
12.5%
0.0003321081881
12.5%
2.0314785 × 10-51
12.5%
0.0001108760551
12.5%
5.904675 × 10-61
12.5%
-0.0003019705821
12.5%
ValueCountFrequency (%)
-0.0003019705821
12.5%
4.86122 × 10-71
12.5%
5.904675 × 10-61
12.5%
6.808959 × 10-61
12.5%
2.0314785 × 10-51
12.5%
0.0001108760551
12.5%
0.0003321081881
12.5%
15.692699991
12.5%
ValueCountFrequency (%)
15.692699991
12.5%
0.0003321081881
12.5%
0.0001108760551
12.5%
2.0314785 × 10-51
12.5%
6.808959 × 10-61
12.5%
5.904675 × 10-61
12.5%
4.86122 × 10-71
12.5%
-0.0003019705821
12.5%

PENRT
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.17364
Minimum-113.94266
Maximum1209.0892
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:32.453721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-113.94266
5-th percentile-73.944264
Q11.412248
median78.217099
Q3143.71871
95-th percentile855.24723
Maximum1209.0892
Range1323.0318
Interquartile range (IQR)142.30646

Descriptive statistics

Standard deviation419.4212
Coefficient of variation (CV)2.1271667
Kurtosis6.8584365
Mean197.17364
Median Absolute Deviation (MAD)77.162779
Skewness2.5484042
Sum1577.3891
Variance175914.14
MonotonicityNot monotonic
2025-11-25T14:51:32.563139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1209.0891871
12.5%
66.401771231
12.5%
0.3384648581
12.5%
125.58755551
12.5%
198.11217161
12.5%
90.032427521
12.5%
1.7701757481
12.5%
-113.94265641
12.5%
ValueCountFrequency (%)
-113.94265641
12.5%
0.3384648581
12.5%
1.7701757481
12.5%
66.401771231
12.5%
90.032427521
12.5%
125.58755551
12.5%
198.11217161
12.5%
1209.0891871
12.5%
ValueCountFrequency (%)
1209.0891871
12.5%
198.11217161
12.5%
125.58755551
12.5%
90.032427521
12.5%
66.401771231
12.5%
1.7701757481
12.5%
0.3384648581
12.5%
-113.94265641
12.5%

SM
Categorical

High correlation 

Distinct2
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size613.0 B
0.0
20.59008

Length

Max length8
Median length3
Mean length3.625
Min length3

Characters and Unicode

Total characters29
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row20.59008
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07
87.5%
20.590081
 
12.5%

Length

2025-11-25T14:51:32.686309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:32.797569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07
87.5%
20.590081
 
12.5%

Most occurring characters

ValueCountFrequency (%)
017
58.6%
.8
27.6%
21
 
3.4%
51
 
3.4%
91
 
3.4%
81
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)29
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
017
58.6%
.8
27.6%
21
 
3.4%
51
 
3.4%
91
 
3.4%
81
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
017
58.6%
.8
27.6%
21
 
3.4%
51
 
3.4%
91
 
3.4%
81
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
017
58.6%
.8
27.6%
21
 
3.4%
51
 
3.4%
91
 
3.4%
81
 
3.4%

RSF
Categorical

High correlation 

Distinct2
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size614.0 B
0.0
227.59632

Length

Max length9
Median length3
Mean length3.75
Min length3

Characters and Unicode

Total characters30
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row227.59632
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07
87.5%
227.596321
 
12.5%

Length

2025-11-25T14:51:32.915924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:33.016953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07
87.5%
227.596321
 
12.5%

Most occurring characters

ValueCountFrequency (%)
014
46.7%
.8
26.7%
23
 
10.0%
71
 
3.3%
51
 
3.3%
91
 
3.3%
61
 
3.3%
31
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)30
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014
46.7%
.8
26.7%
23
 
10.0%
71
 
3.3%
51
 
3.3%
91
 
3.3%
61
 
3.3%
31
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014
46.7%
.8
26.7%
23
 
10.0%
71
 
3.3%
51
 
3.3%
91
 
3.3%
61
 
3.3%
31
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014
46.7%
.8
26.7%
23
 
10.0%
71
 
3.3%
51
 
3.3%
91
 
3.3%
61
 
3.3%
31
 
3.3%

NRSF
Categorical

High correlation 

Distinct2
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size614.0 B
0.0
544.44432

Length

Max length9
Median length3
Mean length3.75
Min length3

Characters and Unicode

Total characters30
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row544.44432
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07
87.5%
544.444321
 
12.5%

Length

2025-11-25T14:51:33.142915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:33.253458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07
87.5%
544.444321
 
12.5%

Most occurring characters

ValueCountFrequency (%)
014
46.7%
.8
26.7%
45
 
16.7%
51
 
3.3%
31
 
3.3%
21
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)30
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
014
46.7%
.8
26.7%
45
 
16.7%
51
 
3.3%
31
 
3.3%
21
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)30
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
014
46.7%
.8
26.7%
45
 
16.7%
51
 
3.3%
31
 
3.3%
21
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)30
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
014
46.7%
.8
26.7%
45
 
16.7%
51
 
3.3%
31
 
3.3%
21
 
3.3%

FW
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02523947
Minimum-3.254817
Maximum3.1773417
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:33.345886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-3.254817
5-th percentile-2.1156005
Q10.001980465
median0.0068287807
Q30.085314804
95-th percentile2.1436449
Maximum3.1773417
Range6.4321587
Interquartile range (IQR)0.083334339

Descriptive statistics

Standard deviation1.7211296
Coefficient of variation (CV)68.191989
Kurtosis3.4797113
Mean0.02523947
Median Absolute Deviation (MAD)0.019512507
Skewness-0.14805805
Sum0.20191576
Variance2.9622872
MonotonicityNot monotonic
2025-11-25T14:51:33.445828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.1773417041
12.5%
0.0026115345131
12.5%
0.22392240351
12.5%
0.0058659498911
12.5%
0.0077916115151
12.5%
0.039112271141
12.5%
8.725629 × 10-51
12.5%
-3.2548169731
12.5%
ValueCountFrequency (%)
-3.2548169731
12.5%
8.725629 × 10-51
12.5%
0.0026115345131
12.5%
0.0058659498911
12.5%
0.0077916115151
12.5%
0.039112271141
12.5%
0.22392240351
12.5%
3.1773417041
12.5%
ValueCountFrequency (%)
3.1773417041
12.5%
0.22392240351
12.5%
0.039112271141
12.5%
0.0077916115151
12.5%
0.0058659498911
12.5%
0.0026115345131
12.5%
8.725629 × 10-51
12.5%
-3.2548169731
12.5%

HWD
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12046759
Minimum-0.00048112896
Maximum0.96148088
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:33.561121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.00048112896
5-th percentile-0.00031205122
Q18.5319125 × 10-6
median0.00035454526
Q30.00089936073
95-th percentile0.62539309
Maximum0.96148088
Range0.96196201
Interquartile range (IQR)0.00089082881

Descriptive statistics

Standard deviation0.33982108
Coefficient of variation (CV)2.8208508
Kurtosis7.9999437
Mean0.12046759
Median Absolute Deviation (MAD)0.00039359063
Skewness2.8284139
Sum0.96374069
Variance0.11547837
MonotonicityNot monotonic
2025-11-25T14:51:33.673117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.96148087641
12.5%
0.0004122784511
12.5%
1.950298 × 10-61
12.5%
0.000789131551
12.5%
0.0012300482551
12.5%
0.0002968120791
12.5%
1.0725784 × 10-51
12.5%
-0.0004811289621
12.5%
ValueCountFrequency (%)
-0.0004811289621
12.5%
1.950298 × 10-61
12.5%
1.0725784 × 10-51
12.5%
0.0002968120791
12.5%
0.0004122784511
12.5%
0.000789131551
12.5%
0.0012300482551
12.5%
0.96148087641
12.5%
ValueCountFrequency (%)
0.96148087641
12.5%
0.0012300482551
12.5%
0.000789131551
12.5%
0.0004122784511
12.5%
0.0002968120791
12.5%
1.0725784 × 10-51
12.5%
1.950298 × 10-61
12.5%
-0.0004811289621
12.5%

NHWD
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.880288
Minimum-0.069852585
Maximum95.95063
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:33.785856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.069852585
5-th percentile-0.044338384
Q10.0072959351
median0.024110441
Q37.3974168
95-th percentile65.118029
Maximum95.95063
Range96.020483
Interquartile range (IQR)7.3901208

Descriptive statistics

Standard deviation33.336911
Coefficient of variation (CV)2.4017449
Kurtosis7.7462543
Mean13.880288
Median Absolute Deviation (MAD)0.057514168
Skewness2.7705956
Sum111.0423
Variance1111.3496
MonotonicityNot monotonic
2025-11-25T14:51:33.895770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7.8574850181
12.5%
0.0030451319381
12.5%
7.24406071
12.5%
0.0087128695381
12.5%
0.0090852655891
12.5%
0.039135616751
12.5%
95.950630371
12.5%
-0.069852585211
12.5%
ValueCountFrequency (%)
-0.069852585211
12.5%
0.0030451319381
12.5%
0.0087128695381
12.5%
0.0090852655891
12.5%
0.039135616751
12.5%
7.24406071
12.5%
7.8574850181
12.5%
95.950630371
12.5%
ValueCountFrequency (%)
95.950630371
12.5%
7.8574850181
12.5%
7.24406071
12.5%
0.039135616751
12.5%
0.0090852655891
12.5%
0.0087128695381
12.5%
0.0030451319381
12.5%
-0.069852585211
12.5%

RWD
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00066077727
Minimum-9.3966036 × 10-5
Maximum0.0052349311
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:34.005669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.3966036 × 10-5
5-th percentile-6.1043362 × 10-5
Q11.03505 × 10-7
median5.5542775 × 10-6
Q34.147391 × 10-5
95-th percentile0.0034439924
Maximum0.0052349311
Range0.0053288972
Interquartile range (IQR)4.1370405 × 10-5

Descriptive statistics

Standard deviation0.0018491138
Coefficient of variation (CV)2.798392
Kurtosis7.977245
Mean0.00066077727
Median Absolute Deviation (MAD)7.9393645 × 10-6
Skewness2.8230764
Sum0.0052862182
Variance3.4192219 × 10-6
MonotonicityNot monotonic
2025-11-25T14:51:34.115830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0052349311341
12.5%
5.355212 × 10-61
12.5%
1.05091 × 10-71
12.5%
5.753343 × 10-61
12.5%
1.5977476 × 10-51
12.5%
0.0001179632121
12.5%
9.8747 × 10-81
12.5%
-9.3966036 × 10-51
12.5%
ValueCountFrequency (%)
-9.3966036 × 10-51
12.5%
9.8747 × 10-81
12.5%
1.05091 × 10-71
12.5%
5.355212 × 10-61
12.5%
5.753343 × 10-61
12.5%
1.5977476 × 10-51
12.5%
0.0001179632121
12.5%
0.0052349311341
12.5%
ValueCountFrequency (%)
0.0052349311341
12.5%
0.0001179632121
12.5%
1.5977476 × 10-51
12.5%
5.753343 × 10-61
12.5%
5.355212 × 10-61
12.5%
1.05091 × 10-71
12.5%
9.8747 × 10-81
12.5%
-9.3966036 × 10-51
12.5%

CRU
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size592.0 B
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08
100.0%

Length

2025-11-25T14:51:34.253842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:34.374199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
08
100.0%

Most occurring characters

ValueCountFrequency (%)
08
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08
100.0%

MFR
Categorical

High correlation 

Distinct3
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Memory size625.0 B
0.0
0.3528
2334.094021853426

Length

Max length17
Median length3
Mean length5.125
Min length3

Characters and Unicode

Total characters41
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)25.0%

Sample

1st row0.3528
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06
75.0%
0.35281
 
12.5%
2334.0940218534261
 
12.5%

Length

2025-11-25T14:51:34.514053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:34.645866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.06
75.0%
0.35281
 
12.5%
2334.0940218534261
 
12.5%

Most occurring characters

ValueCountFrequency (%)
015
36.6%
.8
19.5%
34
 
9.8%
24
 
9.8%
43
 
7.3%
52
 
4.9%
82
 
4.9%
91
 
2.4%
11
 
2.4%
61
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)41
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
015
36.6%
.8
19.5%
34
 
9.8%
24
 
9.8%
43
 
7.3%
52
 
4.9%
82
 
4.9%
91
 
2.4%
11
 
2.4%
61
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)41
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
015
36.6%
.8
19.5%
34
 
9.8%
24
 
9.8%
43
 
7.3%
52
 
4.9%
82
 
4.9%
91
 
2.4%
11
 
2.4%
61
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)41
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
015
36.6%
.8
19.5%
34
 
9.8%
24
 
9.8%
43
 
7.3%
52
 
4.9%
82
 
4.9%
91
 
2.4%
11
 
2.4%
61
 
2.4%

MER
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size592.0 B
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08
100.0%

Length

2025-11-25T14:51:34.895799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:35.001990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
08
100.0%

Most occurring characters

ValueCountFrequency (%)
08
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08
100.0%

EEE
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size592.0 B
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08
100.0%

Length

2025-11-25T14:51:35.115783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:35.231975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
08
100.0%

Most occurring characters

ValueCountFrequency (%)
08
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08
100.0%

EET
Categorical

Constant 

Distinct1
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size592.0 B
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08
100.0%

Length

2025-11-25T14:51:35.345936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T14:51:35.455840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
08
100.0%

Most occurring characters

ValueCountFrequency (%)
08
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08
100.0%

AP (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.054077389
Minimum-0.044628297
Maximum0.29822304
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:35.550993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.044628297
5-th percentile-0.028586586
Q10.0017060882
median0.014949025
Q30.066578193
95-th percentile0.22391549
Maximum0.29822304
Range0.34285134
Interquartile range (IQR)0.064872105

Descriptive statistics

Standard deviation0.10625029
Coefficient of variation (CV)1.9647822
Kurtosis4.9712363
Mean0.054077389
Median Absolute Deviation (MAD)0.02946359
Skewness2.0975823
Sum0.43261911
Variance0.011289124
MonotonicityNot monotonic
2025-11-25T14:51:35.673595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.29822304231
12.5%
0.010432725531
12.5%
0.0018730631231
12.5%
0.085915743271
12.5%
0.060132343491
12.5%
0.019465324381
12.5%
0.0012051635531
12.5%
-0.044628296861
12.5%
ValueCountFrequency (%)
-0.044628296861
12.5%
0.0012051635531
12.5%
0.0018730631231
12.5%
0.010432725531
12.5%
0.019465324381
12.5%
0.060132343491
12.5%
0.085915743271
12.5%
0.29822304231
12.5%
ValueCountFrequency (%)
0.29822304231
12.5%
0.085915743271
12.5%
0.060132343491
12.5%
0.019465324381
12.5%
0.010432725531
12.5%
0.0018730631231
12.5%
0.0012051635531
12.5%
-0.044628296861
12.5%

GWPtotal (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.103493
Minimum-7.783238
Maximum216.2971
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)25.0%
Memory size192.0 B
2025-11-25T14:51:35.788433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-7.783238
5-th percentile-5.5975047
Q10.028820605
median5.1561241
Q310.235452
95-th percentile145.49749
Maximum216.2971
Range224.08034
Interquartile range (IQR)10.206632

Descriptive statistics

Standard deviation75.525342
Coefficient of variation (CV)2.5088565
Kurtosis7.8161937
Mean30.103493
Median Absolute Deviation (MAD)5.6496723
Skewness2.7847974
Sum240.82794
Variance5704.0773
MonotonicityNot monotonic
2025-11-25T14:51:35.901083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
216.29710411
12.5%
4.646304051
12.5%
0.5511894421
12.5%
8.9764421661
12.5%
14.012482211
12.5%
5.6659441491
12.5%
-1.5382859081
12.5%
-7.7832379611
12.5%
ValueCountFrequency (%)
-7.7832379611
12.5%
-1.5382859081
12.5%
0.5511894421
12.5%
4.646304051
12.5%
5.6659441491
12.5%
8.9764421661
12.5%
14.012482211
12.5%
216.29710411
12.5%
ValueCountFrequency (%)
216.29710411
12.5%
14.012482211
12.5%
8.9764421661
12.5%
5.6659441491
12.5%
4.646304051
12.5%
0.5511894421
12.5%
-1.5382859081
12.5%
-7.7832379611
12.5%

GWPbiogenic (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.28945896
Minimum-1.6679278
Maximum0.08526135
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)37.5%
Memory size192.0 B
2025-11-25T14:51:36.026925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.6679278
5-th percentile-1.3039655
Q1-0.24135962
median0.00079712636
Q30.0023105527
95-th percentile0.056901882
Maximum0.08526135
Range1.7531891
Interquartile range (IQR)0.24367017

Descriptive statistics

Standard deviation0.6007211
Coefficient of variation (CV)-2.0753239
Kurtosis4.7712233
Mean-0.28945896
Median Absolute Deviation (MAD)0.043950697
Skewness-2.197596
Sum-2.3156717
Variance0.36086584
MonotonicityNot monotonic
2025-11-25T14:51:36.154074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
-0.62803537641
12.5%
0.0014192203361
12.5%
0.0001750323771
12.5%
0.001669304431
12.5%
0.004234297391
12.5%
0.085261350271
12.5%
-1.6679277991
12.5%
-0.11246769831
12.5%
ValueCountFrequency (%)
-1.6679277991
12.5%
-0.62803537641
12.5%
-0.11246769831
12.5%
0.0001750323771
12.5%
0.0014192203361
12.5%
0.001669304431
12.5%
0.004234297391
12.5%
0.085261350271
12.5%
ValueCountFrequency (%)
0.085261350271
12.5%
0.004234297391
12.5%
0.001669304431
12.5%
0.0014192203361
12.5%
0.0001750323771
12.5%
-0.11246769831
12.5%
-0.62803537641
12.5%
-1.6679277991
12.5%

GWPfossil (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.39182
Minimum-7.6703605
Maximum216.91666
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:36.276041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-7.6703605
5-th percentile-4.9403619
Q10.44566817
median5.112617
Q310.232798
95-th percentile145.89862
Maximum216.91666
Range224.58702
Interquartile range (IQR)9.7871297

Descriptive statistics

Standard deviation75.642172
Coefficient of variation (CV)2.4888991
Kurtosis7.8272295
Mean30.39182
Median Absolute Deviation (MAD)4.7722931
Skewness2.7873515
Sum243.13456
Variance5721.7382
MonotonicityNot monotonic
2025-11-25T14:51:36.397908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
216.9166561
12.5%
4.6447928251
12.5%
0.5510124381
12.5%
8.9744060441
12.5%
14.007973381
12.5%
5.580441151
12.5%
0.12963538311
12.5%
-7.6703605051
12.5%
ValueCountFrequency (%)
-7.6703605051
12.5%
0.12963538311
12.5%
0.5510124381
12.5%
4.6447928251
12.5%
5.580441151
12.5%
8.9744060441
12.5%
14.007973381
12.5%
216.9166561
12.5%
ValueCountFrequency (%)
216.9166561
12.5%
14.007973381
12.5%
8.9744060441
12.5%
5.580441151
12.5%
4.6447928251
12.5%
0.5510124381
12.5%
0.12963538311
12.5%
-7.6703605051
12.5%

GWPluluc (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00237045
Minimum-0.00040980357
Maximum0.018390162
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:36.505899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.00040980357
5-th percentile-0.00026568223
Q15.3742768 × 10-6
median0.00016676285
Q30.00029754068
95-th percentile0.012081976
Maximum0.018390162
Range0.018799965
Interquartile range (IQR)0.00029216641

Descriptive statistics

Standard deviation0.0064773276
Coefficient of variation (CV)2.7325308
Kurtosis7.9676894
Mean0.00237045
Median Absolute Deviation (MAD)0.00016252277
Skewness2.8207109
Sum0.0189636
Variance4.1955773 × 10-5
MonotonicityNot monotonic
2025-11-25T14:51:36.627034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.018390161561
12.5%
9.1992523 × 10-51
12.5%
1.971688 × 10-61
12.5%
0.0003667732561
12.5%
0.0002744631591
12.5%
0.000241533171
12.5%
6.508473 × 10-61
12.5%
-0.0004098035681
12.5%
ValueCountFrequency (%)
-0.0004098035681
12.5%
1.971688 × 10-61
12.5%
6.508473 × 10-61
12.5%
9.1992523 × 10-51
12.5%
0.000241533171
12.5%
0.0002744631591
12.5%
0.0003667732561
12.5%
0.018390161561
12.5%
ValueCountFrequency (%)
0.018390161561
12.5%
0.0003667732561
12.5%
0.0002744631591
12.5%
0.000241533171
12.5%
9.1992523 × 10-51
12.5%
6.508473 × 10-61
12.5%
1.971688 × 10-61
12.5%
-0.0004098035681
12.5%

ETPfw (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.340328
Minimum-26.851679
Maximum82.947315
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:36.741066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-26.851679
5-th percentile-17.328228
Q10.67754544
median18.511832
Q335.115263
95-th percentile73.677171
Maximum82.947315
Range109.79899
Interquartile range (IQR)34.437717

Descriptive statistics

Standard deviation34.692656
Coefficient of variation (CV)1.5529162
Kurtosis0.11312677
Mean22.340328
Median Absolute Deviation (MAD)17.940741
Skewness0.55159614
Sum178.72262
Variance1203.5804
MonotonicityNot monotonic
2025-11-25T14:51:36.851272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
27.415006351
12.5%
27.999953961
12.5%
0.35818175851
12.5%
56.461188161
12.5%
82.947315141
12.5%
9.6086574911
12.5%
0.78399999741
12.5%
-26.851679221
12.5%
ValueCountFrequency (%)
-26.851679221
12.5%
0.35818175851
12.5%
0.78399999741
12.5%
9.6086574911
12.5%
27.415006351
12.5%
27.999953961
12.5%
56.461188161
12.5%
82.947315141
12.5%
ValueCountFrequency (%)
82.947315141
12.5%
56.461188161
12.5%
27.999953961
12.5%
27.415006351
12.5%
9.6086574911
12.5%
0.78399999741
12.5%
0.35818175851
12.5%
-26.851679221
12.5%

PM (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.691207 × 10-6
Minimum-9.41569 × 10-7
Maximum1.7575724 × 10-5
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:36.969671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.41569 × 10-7
5-th percentile-6.003372 × 10-7
Q15.20975 × 10-8
median5.846735 × 10-7
Q31.514084 × 10-6
95-th percentile1.2272442 × 10-5
Maximum1.7575724 × 10-5
Range1.8517293 × 10-5
Interquartile range (IQR)1.4619865 × 10-6

Descriptive statistics

Standard deviation6.0940737 × 10-6
Coefficient of variation (CV)2.2644389
Kurtosis0
Mean2.691207 × 10-6
Median Absolute Deviation (MAD)5.88785 × 10-7
Skewness0
Sum2.1529656 × 10-5
Variance3.7137735 × 10-11
MonotonicityNot monotonic
2025-11-25T14:51:37.088890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7.90073 × 10-71
12.5%
3.79274 × 10-71
12.5%
5.8337 × 10-81
12.5%
2.423489 × 10-61
12.5%
1.210949 × 10-61
12.5%
1.7575724 × 10-51
12.5%
3.3379 × 10-81
12.5%
-9.41569 × 10-71
12.5%
ValueCountFrequency (%)
-9.41569 × 10-71
12.5%
3.3379 × 10-81
12.5%
5.8337 × 10-81
12.5%
3.79274 × 10-71
12.5%
7.90073 × 10-71
12.5%
1.210949 × 10-61
12.5%
2.423489 × 10-61
12.5%
1.7575724 × 10-51
12.5%
ValueCountFrequency (%)
1.7575724 × 10-51
12.5%
2.423489 × 10-61
12.5%
1.210949 × 10-61
12.5%
7.90073 × 10-71
12.5%
3.79274 × 10-71
12.5%
5.8337 × 10-81
12.5%
3.3379 × 10-81
12.5%
-9.41569 × 10-71
12.5%

EPmarine (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.029391693
Minimum-0.016777601
Maximum0.17404558
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:37.212017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.016777601
5-th percentile-0.010713564
Q10.00085680904
median0.0046276119
Q30.030142244
95-th percentile0.12725884
Maximum0.17404558
Range0.19082318
Interquartile range (IQR)0.029285435

Descriptive statistics

Standard deviation0.061005821
Coefficient of variation (CV)2.0756144
Kurtosis6.1028518
Mean0.029391693
Median Absolute Deviation (MAD)0.012742303
Skewness2.3928797
Sum0.23513354
Variance0.0037217102
MonotonicityNot monotonic
2025-11-25T14:51:37.336020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.17404557791
12.5%
0.003851857381
12.5%
0.0009596728911
12.5%
0.040369188441
12.5%
0.026733262471
12.5%
0.0054033665071
12.5%
0.0005482174751
12.5%
-0.016777600581
12.5%
ValueCountFrequency (%)
-0.016777600581
12.5%
0.0005482174751
12.5%
0.0009596728911
12.5%
0.003851857381
12.5%
0.0054033665071
12.5%
0.026733262471
12.5%
0.040369188441
12.5%
0.17404557791
12.5%
ValueCountFrequency (%)
0.17404557791
12.5%
0.040369188441
12.5%
0.026733262471
12.5%
0.0054033665071
12.5%
0.003851857381
12.5%
0.0009596728911
12.5%
0.0005482174751
12.5%
-0.016777600581
12.5%

EPfreshwater (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00098065687
Minimum-0.00085243017
Maximum0.0076229254
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:37.465745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.00085243017
5-th percentile-0.00055272353
Q12.5005902 × 10-5
median8.1657548 × 10-5
Q30.00031478829
95-th percentile0.0051941979
Maximum0.0076229254
Range0.0084753556
Interquartile range (IQR)0.00028978239

Descriptive statistics

Standard deviation0.0027166455
Coefficient of variation (CV)2.7702304
Kurtosis7.4457539
Mean0.00098065687
Median Absolute Deviation (MAD)9.3970939 × 10-5
Skewness2.6899307
Sum0.0078452549
Variance7.3801626 × 10-6
MonotonicityNot monotonic
2025-11-25T14:51:37.595927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0076229253991
12.5%
3.2049696 × 10-51
12.5%
0.0001918163961
12.5%
6.7693625 × 10-51
12.5%
9.5621471 × 10-51
12.5%
0.0006837039881
12.5%
3.874519 × 10-61
12.5%
-0.0008524301671
12.5%
ValueCountFrequency (%)
-0.0008524301671
12.5%
3.874519 × 10-61
12.5%
3.2049696 × 10-51
12.5%
6.7693625 × 10-51
12.5%
9.5621471 × 10-51
12.5%
0.0001918163961
12.5%
0.0006837039881
12.5%
0.0076229253991
12.5%
ValueCountFrequency (%)
0.0076229253991
12.5%
0.0006837039881
12.5%
0.0001918163961
12.5%
9.5621471 × 10-51
12.5%
6.7693625 × 10-51
12.5%
3.2049696 × 10-51
12.5%
3.874519 × 10-61
12.5%
-0.0008524301671
12.5%

EPterrestrial (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23214798
Minimum-0.18149803
Maximum1.1975657
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:37.702080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.18149803
5-th percentile-0.11588901
Q10.0094034983
median0.049176895
Q30.32517978
95-th percentile0.93207494
Maximum1.1975657
Range1.3790637
Interquartile range (IQR)0.31577628

Descriptive statistics

Standard deviation0.43364696
Coefficient of variation (CV)1.8679765
Kurtosis3.894761
Mean0.23214798
Median Absolute Deviation (MAD)0.13694775
Skewness1.876219
Sum1.8571838
Variance0.18804969
MonotonicityNot monotonic
2025-11-25T14:51:37.822615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.1975657181
12.5%
0.040268193071
12.5%
0.010552555851
12.5%
0.43902063481
12.5%
0.28723282191
12.5%
0.058085596281
12.5%
0.0059563255131
12.5%
-0.18149802991
12.5%
ValueCountFrequency (%)
-0.18149802991
12.5%
0.0059563255131
12.5%
0.010552555851
12.5%
0.040268193071
12.5%
0.058085596281
12.5%
0.28723282191
12.5%
0.43902063481
12.5%
1.1975657181
12.5%
ValueCountFrequency (%)
1.1975657181
12.5%
0.43902063481
12.5%
0.28723282191
12.5%
0.058085596281
12.5%
0.040268193071
12.5%
0.010552555851
12.5%
0.0059563255131
12.5%
-0.18149802991
12.5%

HTPc (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.17 × 10-10
Minimum-1.188 × 10-9
Maximum2.527 × 10-9
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:37.940281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.188 × 10-9
5-th percentile-7.687 × 10-10
Q12.455 × 10-10
median4.42 × 10-10
Q31.13925 × 10-9
95-th percentile2.09405 × 10-9
Maximum2.527 × 10-9
Range3.715 × 10-9
Interquartile range (IQR)8.9375 × 10-10

Descriptive statistics

Standard deviation1.0760426 × 10-9
Coefficient of variation (CV)1.7439913
Kurtosis0
Mean6.17 × 10-10
Median Absolute Deviation (MAD)5.395 × 10-10
Skewness0
Sum4.936 × 10-9
Variance1.1578677 × 10-18
MonotonicityNot monotonic
2025-11-25T14:51:38.080942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.089 × 10-91
12.5%
3.24 × 10-101
12.5%
1.29 × 10-91
12.5%
5.04 × 10-101
12.5%
2.527 × 10-91
12.5%
3.8 × 10-101
12.5%
1 × 10-111
12.5%
-1.188 × 10-91
12.5%
ValueCountFrequency (%)
-1.188 × 10-91
12.5%
1 × 10-111
12.5%
3.24 × 10-101
12.5%
3.8 × 10-101
12.5%
5.04 × 10-101
12.5%
1.089 × 10-91
12.5%
1.29 × 10-91
12.5%
2.527 × 10-91
12.5%
ValueCountFrequency (%)
2.527 × 10-91
12.5%
1.29 × 10-91
12.5%
1.089 × 10-91
12.5%
5.04 × 10-101
12.5%
3.8 × 10-101
12.5%
3.24 × 10-101
12.5%
1 × 10-111
12.5%
-1.188 × 10-91
12.5%

HTPnc (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.822475 × 10-8
Minimum-2.2077 × 10-8
Maximum1.22901 × 10-7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:38.206026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-2.2077 × 10-8
5-th percentile-1.426815 × 10-8
Q19.49875 × 10-9
median1.953 × 10-8
Q33.418575 × 10-8
95-th percentile9.432945 × 10-8
Maximum1.22901 × 10-7
Range1.44978 × 10-7
Interquartile range (IQR)2.4687 × 10-8

Descriptive statistics

Standard deviation4.2901767 × 10-8
Coefficient of variation (CV)1.5200052
Kurtosis0
Mean2.822475 × 10-8
Median Absolute Deviation (MAD)1.57955 × 10-8
Skewness0
Sum2.25798 × 10-7
Variance1.8405616 × 10-15
MonotonicityNot monotonic
2025-11-25T14:51:38.327430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.3256 × 10-81
12.5%
4.1268 × 10-81
12.5%
3.1825 × 10-81
12.5%
1.2587 × 10-81
12.5%
1.22901 × 10-71
12.5%
1.5804 × 10-81
12.5%
2.34 × 10-101
12.5%
-2.2077 × 10-81
12.5%
ValueCountFrequency (%)
-2.2077 × 10-81
12.5%
2.34 × 10-101
12.5%
1.2587 × 10-81
12.5%
1.5804 × 10-81
12.5%
2.3256 × 10-81
12.5%
3.1825 × 10-81
12.5%
4.1268 × 10-81
12.5%
1.22901 × 10-71
12.5%
ValueCountFrequency (%)
1.22901 × 10-71
12.5%
4.1268 × 10-81
12.5%
3.1825 × 10-81
12.5%
2.3256 × 10-81
12.5%
1.5804 × 10-81
12.5%
1.2587 × 10-81
12.5%
2.34 × 10-101
12.5%
-2.2077 × 10-81
12.5%

IRP (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57035009
Minimum-0.37436565
Maximum4.3466171
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:38.446031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.37436565
5-th percentile-0.24318939
Q10.00044964134
median0.025572252
Q30.17000069
95-th percentile2.9890251
Maximum4.3466171
Range4.7209828
Interquartile range (IQR)0.16955105

Descriptive statistics

Standard deviation1.5425386
Coefficient of variation (CV)2.7045469
Kurtosis7.4900492
Mean0.57035009
Median Absolute Deviation (MAD)0.035158155
Skewness2.7083291
Sum4.5628007
Variance2.3794252
MonotonicityNot monotonic
2025-11-25T14:51:38.555789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4.3466171271
12.5%
0.023710099041
12.5%
0.0004236614111
12.5%
0.027434404841
12.5%
0.070739971251
12.5%
0.46778283191
12.5%
0.0004583013111
12.5%
-0.37436564841
12.5%
ValueCountFrequency (%)
-0.37436564841
12.5%
0.0004236614111
12.5%
0.0004583013111
12.5%
0.023710099041
12.5%
0.027434404841
12.5%
0.070739971251
12.5%
0.46778283191
12.5%
4.3466171271
12.5%
ValueCountFrequency (%)
4.3466171271
12.5%
0.46778283191
12.5%
0.070739971251
12.5%
0.027434404841
12.5%
0.023710099041
12.5%
0.0004583013111
12.5%
0.0004236614111
12.5%
-0.37436564841
12.5%

SOP (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.14719878
Minimum-66.180958
Maximum55.859839
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:38.770885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-66.180958
5-th percentile-42.976083
Q10.19723014
median0.57020436
Q32.9043575
95-th percentile38.297883
Maximum55.859839
Range122.0408
Interquartile range (IQR)2.7071274

Descriptive statistics

Standard deviation32.813505
Coefficient of variation (CV)-222.91969
Kurtosis3.633198
Mean-0.14719878
Median Absolute Deviation (MAD)0.92975928
Skewness-0.60406094
Sum-1.1775902
Variance1076.7261
MonotonicityNot monotonic
2025-11-25T14:51:38.925934image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
55.859839011
12.5%
0.11868452311
12.5%
0.7863089951
12.5%
0.22341200821
12.5%
0.35409972631
12.5%
5.6828208161
12.5%
1.9782030821
12.5%
-66.180958371
12.5%
ValueCountFrequency (%)
-66.180958371
12.5%
0.11868452311
12.5%
0.22341200821
12.5%
0.35409972631
12.5%
0.7863089951
12.5%
1.9782030821
12.5%
5.6828208161
12.5%
55.859839011
12.5%
ValueCountFrequency (%)
55.859839011
12.5%
5.6828208161
12.5%
1.9782030821
12.5%
0.7863089951
12.5%
0.35409972631
12.5%
0.22341200821
12.5%
0.11868452311
12.5%
-66.180958371
12.5%

ODP (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6894 × 10-8
Minimum-1.40192 × 10-7
Maximum3.02778 × 10-7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:39.065084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1.40192 × 10-7
5-th percentile-9.09736 × 10-8
Q11.554 × 10-9
median1.136015 × 10-7
Q31.4651475 × 10-7
95-th percentile2.5332195 × 10-7
Maximum3.02778 × 10-7
Range4.4297 × 10-7
Interquartile range (IQR)1.4496075 × 10-7

Descriptive statistics

Standard deviation1.3263072 × 10-7
Coefficient of variation (CV)1.5263507
Kurtosis0
Mean8.6894 × 10-8
Median Absolute Deviation (MAD)7.97735 × 10-8
Skewness0
Sum6.95152 × 10-7
Variance1.7590907 × 10-14
MonotonicityNot monotonic
2025-11-25T14:51:39.171747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1.61475 × 10-71
12.5%
1.01483 × 10-71
12.5%
4.32 × 10-101
12.5%
1.41528 × 10-71
12.5%
3.02778 × 10-71
12.5%
1.2572 × 10-71
12.5%
1.928 × 10-91
12.5%
-1.40192 × 10-71
12.5%
ValueCountFrequency (%)
-1.40192 × 10-71
12.5%
4.32 × 10-101
12.5%
1.928 × 10-91
12.5%
1.01483 × 10-71
12.5%
1.2572 × 10-71
12.5%
1.41528 × 10-71
12.5%
1.61475 × 10-71
12.5%
3.02778 × 10-71
12.5%
ValueCountFrequency (%)
3.02778 × 10-71
12.5%
1.61475 × 10-71
12.5%
1.41528 × 10-71
12.5%
1.2572 × 10-71
12.5%
1.01483 × 10-71
12.5%
1.928 × 10-91
12.5%
4.32 × 10-101
12.5%
-1.40192 × 10-71
12.5%

POCP (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087942125
Minimum-0.059284587
Maximum0.49782615
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:39.274476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.059284587
5-th percentile-0.037910971
Q10.0025942317
median0.019503349
Q30.10141566
95-th percentile0.36880015
Maximum0.49782615
Range0.55711073
Interquartile range (IQR)0.098821426

Descriptive statistics

Standard deviation0.17543358
Coefficient of variation (CV)1.9948754
Kurtosis5.5426027
Mean0.087942125
Median Absolute Deviation (MAD)0.045188919
Skewness2.2522412
Sum0.703537
Variance0.030776941
MonotonicityNot monotonic
2025-11-25T14:51:39.379239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.49782614631
12.5%
0.01723878811
12.5%
0.0028646796471
12.5%
0.12918045341
12.5%
0.09216072521
12.5%
0.02176790931
12.5%
0.0017828877071
12.5%
-0.059284586591
12.5%
ValueCountFrequency (%)
-0.059284586591
12.5%
0.0017828877071
12.5%
0.0028646796471
12.5%
0.01723878811
12.5%
0.02176790931
12.5%
0.09216072521
12.5%
0.12918045341
12.5%
0.49782614631
12.5%
ValueCountFrequency (%)
0.49782614631
12.5%
0.12918045341
12.5%
0.09216072521
12.5%
0.02176790931
12.5%
0.01723878811
12.5%
0.0028646796471
12.5%
0.0017828877071
12.5%
-0.059284586591
12.5%

ADPF (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean193.27302
Minimum-106.01048
Maximum1200.187
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:39.485939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-106.01048
5-th percentile-68.795285
Q11.3288632
median72.779155
Q3135.18425
95-th percentile845.33676
Maximum1200.187
Range1306.1975
Interquartile range (IQR)133.85539

Descriptive statistics

Standard deviation416.16204
Coefficient of variation (CV)2.153234
Kurtosis6.9782189
Mean193.27302
Median Absolute Deviation (MAD)71.787033
Skewness2.5789371
Sum1546.1842
Variance173190.84
MonotonicityNot monotonic
2025-11-25T14:51:39.615656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1200.1870451
12.5%
62.452397991
12.5%
0.31864165091
12.5%
118.13597031
12.5%
186.32908331
12.5%
83.105912481
12.5%
1.6656037311
12.5%
-106.01047671
12.5%
ValueCountFrequency (%)
-106.01047671
12.5%
0.31864165091
12.5%
1.6656037311
12.5%
62.452397991
12.5%
83.105912481
12.5%
118.13597031
12.5%
186.32908331
12.5%
1200.1870451
12.5%
ValueCountFrequency (%)
1200.1870451
12.5%
186.32908331
12.5%
118.13597031
12.5%
83.105912481
12.5%
62.452397991
12.5%
1.6656037311
12.5%
0.31864165091
12.5%
-106.01047671
12.5%

ADPE (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7768388 × 10-7
Minimum-6.39505 × 10-7
Maximum9.50255 × 10-7
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:39.735978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-6.39505 × 10-7
5-th percentile-4.153769 × 10-7
Q14.07775 × 10-9
median1.22918 × 10-7
Q34.032995 × 10-7
95-th percentile7.8626495 × 10-7
Maximum9.50255 × 10-7
Range1.58976 × 10-6
Interquartile range (IQR)3.9922175 × 10-7

Descriptive statistics

Standard deviation4.5777284 × 10-7
Coefficient of variation (CV)2.576333
Kurtosis0
Mean1.7768388 × 10-7
Median Absolute Deviation (MAD)1.881505 × 10-7
Skewness0
Sum1.421471 × 10-6
Variance2.0955597 × 10-13
MonotonicityNot monotonic
2025-11-25T14:51:39.882874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
9.50255 × 10-71
12.5%
1.61457 × 10-71
12.5%
8.61 × 10-101
12.5%
3.77162 × 10-71
12.5%
4.81712 × 10-71
12.5%
8.4379 × 10-81
12.5%
5.15 × 10-91
12.5%
-6.39505 × 10-71
12.5%
ValueCountFrequency (%)
-6.39505 × 10-71
12.5%
8.61 × 10-101
12.5%
5.15 × 10-91
12.5%
8.4379 × 10-81
12.5%
1.61457 × 10-71
12.5%
3.77162 × 10-71
12.5%
4.81712 × 10-71
12.5%
9.50255 × 10-71
12.5%
ValueCountFrequency (%)
9.50255 × 10-71
12.5%
4.81712 × 10-71
12.5%
3.77162 × 10-71
12.5%
1.61457 × 10-71
12.5%
8.4379 × 10-81
12.5%
5.15 × 10-91
12.5%
8.61 × 10-101
12.5%
-6.39505 × 10-71
12.5%

WDP (A2)
Real number (ℝ)

High correlation  Unique 

Distinct8
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.83251035
Minimum-145.76549
Maximum140.65919
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)12.5%
Memory size192.0 B
2025-11-25T14:51:40.011966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-145.76549
5-th percentile-94.746763
Q10.043458771
median0.16095381
Q33.5119726
95-th percentile94.947216
Maximum140.65919
Range286.42468
Interquartile range (IQR)3.4685138

Descriptive statistics

Standard deviation76.653154
Coefficient of variation (CV)92.074716
Kurtosis3.4830601
Mean0.83251035
Median Absolute Deviation (MAD)0.66457553
Skewness-0.17590189
Sum6.6600828
Variance5875.7061
MonotonicityNot monotonic
2025-11-25T14:51:40.131202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
140.65919061
12.5%
0.05717963361
12.5%
10.053548731
12.5%
0.1513100331
12.5%
0.1705975851
12.5%
1.3314472431
12.5%
0.002296182421
12.5%
-145.76548731
12.5%
ValueCountFrequency (%)
-145.76548731
12.5%
0.002296182421
12.5%
0.05717963361
12.5%
0.1513100331
12.5%
0.1705975851
12.5%
1.3314472431
12.5%
10.053548731
12.5%
140.65919061
12.5%
ValueCountFrequency (%)
140.65919061
12.5%
10.053548731
12.5%
1.3314472431
12.5%
0.1705975851
12.5%
0.1513100331
12.5%
0.05717963361
12.5%
0.002296182421
12.5%
-145.76548731
12.5%

Unnamed: 80
Unsupported

Missing  Rejected  Unsupported 

Missing8
Missing (%)100.0%
Memory size192.0 B

Interactions

2025-11-25T14:51:21.946056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:49:53.346110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:49:56.102068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:49:59.106284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-11-25T14:50:39.860166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:43.028018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:46.226127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:49.681136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:52.886590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:56.425975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:59.666057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:02.893329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:05.754019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:08.897430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:12.226166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:15.286575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:18.474054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:21.709299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:25.052974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:49:56.008581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:49:58.995966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:01.892998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:05.036348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:08.563500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:11.648919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:14.689515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:17.606167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:20.682209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:23.991840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:27.326060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:30.136006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:33.345881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:36.411240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:40.006021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:43.135984image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:46.345839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:49.806728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:53.005986image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:56.564257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:50:59.786262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:03.005827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:05.850908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:09.017209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:12.344287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:15.386050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:18.591018image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T14:51:21.825799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T14:51:40.286036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ADPE (A2)ADPF (A2)AP (A2)EPfreshwater (A2)EPmarine (A2)EPterrestrial (A2)ETPfw (A2)FWGWPbiogenic (A2)GWPfossil (A2)GWPluluc (A2)GWPtotal (A2)HTPc (A2)HTPnc (A2)HWDIRP (A2)MFRNHWDNRSFODP (A2)PENREPENRMPENRTPEREPERMPERTPM (A2)POCP (A2)RSFRWDSMSOP (A2)WDP (A2)
ADPE (A2)1.0000.9760.9290.5480.9290.9290.8570.5000.2380.9520.9520.9520.5710.5241.0000.8570.0000.1670.5770.9520.9760.8810.9760.8570.8570.8570.6430.9290.5770.8330.5770.3570.500
ADPF (A2)0.9761.0000.9520.6430.9520.9520.8100.5710.3100.9760.9760.9760.5950.4520.9760.9290.5060.2380.8160.9761.0000.9291.0000.9290.9290.9290.7380.9520.8160.9050.8160.4760.571
AP (A2)0.9290.9521.0000.7141.0001.0000.7620.6670.3570.9760.9760.9760.6430.4290.9290.8810.4330.1670.8160.9050.9520.9520.9520.8810.8810.8810.7861.0000.8160.9050.8160.4290.667
EPfreshwater (A2)0.5480.6430.7141.0000.7140.7140.2620.9760.3100.7380.6190.7380.7140.4760.5480.7620.5650.4760.9130.5710.6430.6670.6430.7620.7620.7620.6430.7140.9130.8570.9130.7620.976
EPmarine (A2)0.9290.9521.0000.7141.0001.0000.7620.6670.3570.9760.9760.9760.6430.4290.9290.8810.6260.1670.8160.9050.9520.9520.9520.8810.8810.8810.7861.0000.8160.9050.8160.4290.667
EPterrestrial (A2)0.9290.9521.0000.7141.0001.0000.7620.6670.3570.9760.9760.9760.6430.4290.9290.8810.0000.1670.7070.9050.9520.9520.9520.8810.8810.8810.7861.0000.7070.9050.7070.4290.667
ETPfw (A2)0.8570.8100.7620.2620.7620.7621.0000.2140.5240.7860.7860.7860.5240.5950.8570.6430.000-0.1190.0000.8810.8100.6900.8100.6430.6430.6430.6900.7620.0000.6190.0000.0000.214
FW0.5000.5710.6670.9760.6670.6670.2141.0000.2140.6900.5480.6900.7860.5240.5000.6430.5650.5000.9130.5000.5710.5710.5710.6430.6430.6430.5240.6670.9130.7620.9130.7141.000
GWPbiogenic (A2)0.2380.3100.3570.3100.3570.3570.5240.2141.0000.3810.2860.3810.3810.4290.2380.3810.383-0.4050.8160.4290.3100.3100.3100.3810.3810.3810.7860.3570.8160.4520.816-0.1190.214
GWPfossil (A2)0.9520.9760.9760.7380.9760.9760.7860.6900.3811.0000.9521.0000.7140.5480.9520.9050.9130.1900.2180.9520.9760.9050.9760.9050.9050.9050.7620.9760.2180.9290.2180.4520.690
GWPluluc (A2)0.9520.9760.9760.6190.9760.9760.7860.5480.2860.9521.0000.9520.5240.3330.9520.9050.9130.2140.2180.9290.9760.9760.9760.9050.9050.9050.7620.9760.2180.8810.2180.4520.548
GWPtotal (A2)0.9520.9760.9760.7380.9760.9760.7860.6900.3811.0000.9521.0000.7140.5480.9520.9050.9130.1900.2180.9520.9760.9050.9760.9050.9050.9050.7620.9760.2180.9290.2180.4520.690
HTPc (A2)0.5710.5950.6430.7140.6430.6430.5240.7860.3810.7140.5240.7141.0000.7380.5710.4760.0000.2860.0000.6430.5950.4290.5950.4760.4760.4760.5000.6430.0000.5950.0000.3570.786
HTPnc (A2)0.5240.4520.4290.4760.4290.4290.5950.5240.4290.5480.3330.5480.7381.0000.5240.3810.0000.0240.0000.5240.4520.2380.4520.3810.3810.3810.3570.4290.0000.4760.0000.0950.524
HWD1.0000.9760.9290.5480.9290.9290.8570.5000.2380.9520.9520.9520.5710.5241.0000.8570.9130.1670.2180.9520.9760.8810.9760.8570.8570.8570.6430.9290.2180.8330.2180.3570.500
IRP (A2)0.8570.9290.8810.7620.8810.8810.6430.6430.3810.9050.9050.9050.4760.3810.8571.0001.0000.3100.9130.8810.9290.9290.9291.0001.0001.0000.8100.8810.9130.9760.9130.6430.643
MFR0.0000.5060.4330.5650.6260.0000.0000.5650.3830.9130.9130.9130.0000.0000.9131.0001.0000.0000.9130.0000.5060.9130.5061.0000.9131.0000.5890.0000.9130.9130.9130.5650.565
NHWD0.1670.2380.1670.4760.1670.167-0.1190.500-0.4050.1900.2140.1900.2860.0240.1670.3100.0001.0000.0000.1670.2380.2380.2380.3100.3100.3100.0240.1670.0000.2620.0000.8810.500
NRSF0.5770.8160.8160.9130.8160.7070.0000.9130.8160.2180.2180.2180.0000.0000.2180.9130.9130.0001.0000.0000.8160.2180.8160.9130.2180.9130.0000.7070.2180.2180.2180.9130.913
ODP (A2)0.9520.9760.9050.5710.9050.9050.8810.5000.4290.9520.9290.9520.6430.5240.9520.8810.0000.1670.0001.0000.9760.8570.9760.8810.8810.8810.7620.9050.0000.8570.0000.3810.500
PENRE0.9761.0000.9520.6430.9520.9520.8100.5710.3100.9760.9760.9760.5950.4520.9760.9290.5060.2380.8160.9761.0000.9291.0000.9290.9290.9290.7380.9520.8160.9050.8160.4760.571
PENRM0.8810.9290.9520.6670.9520.9520.6900.5710.3100.9050.9760.9050.4290.2380.8810.9290.9130.2380.2180.8570.9291.0000.9290.9290.9290.9290.8100.9520.2180.9050.2180.5240.571
PENRT0.9761.0000.9520.6430.9520.9520.8100.5710.3100.9760.9760.9760.5950.4520.9760.9290.5060.2380.8160.9761.0000.9291.0000.9290.9290.9290.7380.9520.8160.9050.8160.4760.571
PERE0.8570.9290.8810.7620.8810.8810.6430.6430.3810.9050.9050.9050.4760.3810.8571.0001.0000.3100.9130.8810.9290.9290.9291.0001.0001.0000.8100.8810.9130.9760.9130.6430.643
PERM0.8570.9290.8810.7620.8810.8810.6430.6430.3810.9050.9050.9050.4760.3810.8571.0000.9130.3100.2180.8810.9290.9290.9291.0001.0001.0000.8100.8810.2180.9760.2180.6430.643
PERT0.8570.9290.8810.7620.8810.8810.6430.6430.3810.9050.9050.9050.4760.3810.8571.0001.0000.3100.9130.8810.9290.9290.9291.0001.0001.0000.8100.8810.9130.9760.9130.6430.643
PM (A2)0.6430.7380.7860.6430.7860.7860.6900.5240.7860.7620.7620.7620.5000.3570.6430.8100.5890.0240.0000.7620.7380.8100.7380.8100.8100.8101.0000.7860.0000.8330.0000.3570.524
POCP (A2)0.9290.9521.0000.7141.0001.0000.7620.6670.3570.9760.9760.9760.6430.4290.9290.8810.0000.1670.7070.9050.9520.9520.9520.8810.8810.8810.7861.0000.7070.9050.7070.4290.667
RSF0.5770.8160.8160.9130.8160.7070.0000.9130.8160.2180.2180.2180.0000.0000.2180.9130.9130.0000.2180.0000.8160.2180.8160.9130.2180.9130.0000.7071.0000.2180.2180.9130.913
RWD0.8330.9050.9050.8570.9050.9050.6190.7620.4520.9290.8810.9290.5950.4760.8330.9760.9130.2620.2180.8570.9050.9050.9050.9760.9760.9760.8330.9050.2181.0000.2180.6190.762
SM0.5770.8160.8160.9130.8160.7070.0000.9130.8160.2180.2180.2180.0000.0000.2180.9130.9130.0000.2180.0000.8160.2180.8160.9130.2180.9130.0000.7070.2180.2181.0000.9130.913
SOP (A2)0.3570.4760.4290.7620.4290.4290.0000.714-0.1190.4520.4520.4520.3570.0950.3570.6430.5650.8810.9130.3810.4760.5240.4760.6430.6430.6430.3570.4290.9130.6190.9131.0000.714
WDP (A2)0.5000.5710.6670.9760.6670.6670.2141.0000.2140.6900.5480.6900.7860.5240.5000.6430.5650.5000.9130.5000.5710.5710.5710.6430.6430.6430.5240.6670.9130.7620.9130.7141.000

Missing values

2025-11-25T14:51:25.398994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T14:51:25.925833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

UUIDVersionName (en)Name (it)Category (original)ComplianceBackground database(s)Location codeTypeReference yearValid untilURLDeclaration ownerPublication dateRegistration numberRegistration authorityPredecessor UUIDPredecessor VersionPredecessor URLRef. quantityRef. unitReference flow UUIDReference flow nameBulk Density (kg/m3)Grammage (kg/m2)Gross Density (kg/m3)Layer Thickness (m)Productiveness (m2)Linear Density (kg/m)Weight Per Piece (kg)Conversion factor to 1kgCarbon content (biogenic) in kgCarbon content (biogenic) - packaging in kgModuleScenarioScenario DescriptionGWPODPPOCPAPEPADPEADPFPEREPERMPERTPENREPENRMPENRTSMRSFNRSFFWHWDNHWDRWDCRUMFRMEREEEEETAP (A2)GWPtotal (A2)GWPbiogenic (A2)GWPfossil (A2)GWPluluc (A2)ETPfw (A2)PM (A2)EPmarine (A2)EPfreshwater (A2)EPterrestrial (A2)HTPc (A2)HTPnc (A2)IRP (A2)SOP (A2)ODP (A2)POCP (A2)ADPF (A2)ADPE (A2)WDP (A2)Unnamed: 80
01a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00A1NaNNaNNaNNaNNaNNaNNaNNaNNaN23.72620631.58700955.3208141193.3458281.569270e+011209.08918720.59008227.59632544.444323.1773420.9614817.8574855.234931e-0300.3528000000.298223216.297104-0.628035216.9166560.01839027.4150067.900730e-070.1740460.0076231.1975661.089000e-092.325600e-084.34661755.8598391.614750e-070.4978261200.1870459.502550e-07140.659191NaN
11a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00A2NaNNaNNaNNaNNaNNaNNaNNaNNaN0.1420800.0220980.16417866.4017596.808959e-0666.4017710.000000.000000.000000.0026120.0004120.0030455.355212e-0600.0000000000.0104334.6463040.0014194.6447930.00009227.9999543.792740e-070.0038520.0000320.0402683.240000e-104.126800e-080.0237100.1186851.014830e-070.01723962.4523981.614570e-070.057180NaN
21a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00A3NaNNaNNaNNaNNaNNaNNaNNaNNaN0.0024920.0005560.0030480.3384644.861220e-070.3384650.000000.000000.000000.2239220.0000027.2440611.050910e-0700.0000000000.0018730.5511890.0001750.5510120.0000020.3581825.833700e-080.0009600.0001920.0105531.290000e-093.182500e-080.0004240.7863094.320000e-100.0028650.3186428.610000e-1010.053549NaN
31a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00C1NaNNaNNaNNaNNaNNaNNaNNaNNaN0.1998450.0301940.230039125.5872193.321082e-04125.5875560.000000.000000.000000.0058660.0007890.0087135.753343e-0600.0000000000.0859168.9764420.0016698.9744060.00036756.4611882.423489e-060.0403690.0000680.4390215.040000e-101.258700e-080.0274340.2234121.415280e-070.129180118.1359703.771620e-070.151310NaN
41a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00C2NaNNaNNaNNaNNaNNaNNaNNaNNaN0.4239000.0659300.489830198.1121502.031478e-05198.1121720.000000.000000.000000.0077920.0012300.0090851.597748e-0500.0000000000.06013214.0124820.00423414.0079730.00027482.9473151.210949e-060.0267330.0000960.2872332.527000e-091.229010e-070.0707400.3541003.027780e-070.092161186.3290834.817120e-070.170598NaN
51a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00C3NaNNaNNaNNaNNaNNaNNaNNaNNaN4.1318091.1323655.26417490.0323151.108761e-0490.0324280.000000.000000.000000.0391120.0002970.0391361.179632e-0402334.0940220000.0194655.6659440.0852615.5804410.0002429.6086571.757572e-050.0054030.0006840.0580863.800000e-101.580400e-080.4677835.6828211.257200e-070.02176883.1059128.437900e-081.331447NaN
61a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00C4NaNNaNNaNNaNNaNNaNNaNNaNNaN0.0036610.0037580.0074191.7701705.904675e-061.7701760.000000.000000.000000.0000870.00001195.9506309.874700e-0800.0000000000.001205-1.538286-1.6679280.1296350.0000070.7840003.337900e-080.0005480.0000040.0059561.000000e-112.340000e-100.0004581.9782031.928000e-090.0017831.6656045.150000e-090.002296NaN
71a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00DNaNNaNNaNNaNNaNNaNNaNNaNNaN-1.137262-0.929407-2.066669-113.942355-3.019706e-04-113.9426560.000000.000000.00000-3.254817-0.000481-0.069853-9.396604e-0500.000000000-0.044628-7.783238-0.112468-7.670361-0.000410-26.851679-9.415690e-07-0.016778-0.000852-0.181498-1.188000e-09-2.207700e-08-0.374366-66.180958-1.401920e-07-0.059285-106.010477-6.395050e-07-145.765487NaN
UUIDVersionName (en)Name (it)Category (original)ComplianceBackground database(s)Location codeTypeReference yearValid untilURLDeclaration ownerPublication dateRegistration numberRegistration authorityPredecessor UUIDPredecessor VersionPredecessor URLRef. quantityRef. unitReference flow UUIDReference flow nameBulk Density (kg/m3)Grammage (kg/m2)Gross Density (kg/m3)Layer Thickness (m)Productiveness (m2)Linear Density (kg/m)Weight Per Piece (kg)Conversion factor to 1kgCarbon content (biogenic) in kgCarbon content (biogenic) - packaging in kgModuleScenarioScenario DescriptionGWPODPPOCPAPEPADPEADPFPEREPERMPERTPENREPENRMPENRTSMRSFNRSFFWHWDNHWDRWDCRUMFRMEREEEEETAP (A2)GWPtotal (A2)GWPbiogenic (A2)GWPfossil (A2)GWPluluc (A2)ETPfw (A2)PM (A2)EPmarine (A2)EPfreshwater (A2)EPterrestrial (A2)HTPc (A2)HTPnc (A2)IRP (A2)SOP (A2)ODP (A2)POCP (A2)ADPF (A2)ADPE (A2)WDP (A2)Unnamed: 80
01a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00A1NaNNaNNaNNaNNaNNaNNaNNaNNaN23.72620631.58700955.3208141193.3458281.569270e+011209.08918720.59008227.59632544.444323.1773420.9614817.8574855.234931e-0300.3528000000.298223216.297104-0.628035216.9166560.01839027.4150067.900730e-070.1740460.0076231.1975661.089000e-092.325600e-084.34661755.8598391.614750e-070.4978261200.1870459.502550e-07140.659191NaN
11a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00A2NaNNaNNaNNaNNaNNaNNaNNaNNaN0.1420800.0220980.16417866.4017596.808959e-0666.4017710.000000.000000.000000.0026120.0004120.0030455.355212e-0600.0000000000.0104334.6463040.0014194.6447930.00009227.9999543.792740e-070.0038520.0000320.0402683.240000e-104.126800e-080.0237100.1186851.014830e-070.01723962.4523981.614570e-070.057180NaN
21a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00A3NaNNaNNaNNaNNaNNaNNaNNaNNaN0.0024920.0005560.0030480.3384644.861220e-070.3384650.000000.000000.000000.2239220.0000027.2440611.050910e-0700.0000000000.0018730.5511890.0001750.5510120.0000020.3581825.833700e-080.0009600.0001920.0105531.290000e-093.182500e-080.0004240.7863094.320000e-100.0028650.3186428.610000e-1010.053549NaN
31a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00C1NaNNaNNaNNaNNaNNaNNaNNaNNaN0.1998450.0301940.230039125.5872193.321082e-04125.5875560.000000.000000.000000.0058660.0007890.0087135.753343e-0600.0000000000.0859168.9764420.0016698.9744060.00036756.4611882.423489e-060.0403690.0000680.4390215.040000e-101.258700e-080.0274340.2234121.415280e-070.129180118.1359703.771620e-070.151310NaN
41a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00C2NaNNaNNaNNaNNaNNaNNaNNaNNaN0.4239000.0659300.489830198.1121502.031478e-05198.1121720.000000.000000.000000.0077920.0012300.0090851.597748e-0500.0000000000.06013214.0124820.00423414.0079730.00027482.9473151.210949e-060.0267330.0000960.2872332.527000e-091.229010e-070.0707400.3541003.027780e-070.092161186.3290834.817120e-070.170598NaN
51a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00C3NaNNaNNaNNaNNaNNaNNaNNaNNaN4.1318091.1323655.26417490.0323151.108761e-0490.0324280.000000.000000.000000.0391120.0002970.0391361.179632e-0402334.0940220000.0194655.6659440.0852615.5804410.0002429.6086571.757572e-050.0054030.0006840.0580863.800000e-101.580400e-080.4677835.6828211.257200e-070.02176883.1059128.437900e-081.331447NaN
61a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00C4NaNNaNNaNNaNNaNNaNNaNNaNNaN0.0036610.0037580.0074191.7701705.904675e-061.7701760.000000.000000.000000.0000870.00001195.9506309.874700e-0800.0000000000.001205-1.538286-1.6679280.1296350.0000070.7840003.337900e-080.0005480.0000040.0059561.000000e-112.340000e-100.0004581.9782031.928000e-090.0017831.6656045.150000e-090.002296NaN
71a0d36c1-153e-4983-ab58-3d879ec4645800.03.0011 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaN'EN 15804+A2' / 'ISO 14025' / 'ISO 21930''ecoinvent database (b497a91f-e14b-4b69-8f28-f50eb1576766)'ITspecific dataset20242029https://node.epditaly.it/resource/processes/1a0d36c1-153e-4983-ab58-3d879ec46458?version=00.03.001Betonrossi S.p.A.2024-09-11EPDITALY0737EPDItalyNaNNaNNaN1m3325f0ef1-ce83-4c24-9f6d-9d4ac43504871 Ready-mixed concrete mixtures: Multibeton R30C3D16S4XC2XC1NaNNaNNaNNaNNaNNaNNaNNaN00DNaNNaNNaNNaNNaNNaNNaNNaNNaN-1.137262-0.929407-2.066669-113.942355-3.019706e-04-113.9426560.000000.000000.00000-3.254817-0.000481-0.069853-9.396604e-0500.000000000-0.044628-7.783238-0.112468-7.670361-0.000410-26.851679-9.415690e-07-0.016778-0.000852-0.181498-1.188000e-09-2.207700e-08-0.374366-66.180958-1.401920e-07-0.059285-106.010477-6.395050e-07-145.765487NaN